The paper introduces Space-sampled Value Decay (SsVD), a forgetting mechanism designed for non-stationary reinforcement learning where the environment can drift at every timestep. SsVD selectively pulls value estimates of randomly chosen state-space elements toward a baseline, discarding outdated information without requiring reset or change-point detection. Integrated into Soft Actor Critic and Deep Q-Networks, SsVD outperforms its base algorithms across six non-stationary environments and can also promote optimism in hard-exploration tasks.
By Felix St\"orck, Philipp Hartmann, Fabian Hinder, Klaus Neumann, Barbara Hammer
arXiv:2511. 03836v2 Announce Type: replace Abstract: Deep Q-Networks (DQNs) estimate future returns by learning from transitions sampled from a replay buffer.
By Lipeng Zu, Hansong Zhou, Xiaonan Zhang
arXiv:2607. 04364v1 Announce Type: new Abstract: Continual post-training is becoming a central paradigm for adapting vision-language models to evolving tasks.
By Mao-Lin Luo, Zhe-Xu Wang, Zi-Hao Zhou, Bo Ye, Jian Zhao, Min-Ling Zhang, Tong Wei
arXiv:2603. 11395v3 Announce Type: replace-cross Abstract: Continual reinforcement learning challenges agents to acquire new skills while retaining previously learned ones with the goal of improving performance in both past and future tasks.
By Abdulaziz Alyahya, Abdallah Al Siyabi, Markus R. Ernst, Luke Yang, Levin Kuhlmann, Gideon Kowadlo
arXiv:2606. 00350v1 Announce Type: cross Abstract: Offline reinforcement learning requires improving a policy from fixed data while avoiding out-of-distribution actions with unreliable value estimates.
By Anas Houssaini, Mohamad H. Danesh, Amin Abyaneh, Scott Fujimoto, Hsiu-Chin Lin, David Meger
arXiv:2410.14606v3 Announce Type: replace
Abstract: Learning from a stream of experience as it arrives, also known as streaming learning, is a core part of natural learning. However, reliable streami...
By Mohamed Elsayed, Elena Sorina Lupu, Gautham Vasan, A. Rupam Mahmood
arXiv:2603. 11653v2 Announce Type: replace Abstract: Continual Reinforcement Learning (CRL) for Vision-Language-Action (VLA) models is a promising direction toward self-improving embodied agents that can adapt in openended, evolving environments.
By Jiaheng Hu, Jay Shim, Chen Tang, Yoonchang Sung, Bo Liu, Peter Stone, Roberto Martin-Martin
arXiv:2607. 07847v1 Announce Type: new Abstract: As large language models (LLMs) become increasingly capable, the next question is how can we enable models to continually learn?
By Anne Harrington, Nayan Saxena, Michael Murphy, Anastasia Borovykh, Zeyu Yun, Sridhar Kamath, Ara Eindra Kyi, Trevor Darrell, Jitendra Malik, Yutong Bai
The paper introduces Uncertainty-Driven Replay Memory (UDRM), a new experience replay buffer for reinforcement learning that prioritizes storing transitions with high uncertainty estimates. Unlike traditional buffers that rely on temporal difference error or transition distributions, UDRM updates its contents based on uncertainty derived from the RL model during training. Experiments show that this uncertainty-aware buffer leads to higher rewards during training compared to other uncertainty-aware RL frameworks.
By Sheeraja Rajakrishnan, Alexander G. Ororbia, Travis Desell, Daniel E. Krutz
arXiv:2606. 00880v1 Announce Type: cross Abstract: Continual reinforcement learning aims to produce agents that learn not only to improve at their current tasks but also to adapt as task distributions change.
By Purab Seth, Neil Shah, Kunal Jha, Samuel J. Gershman, Max Kleiman-Weiner, Wilka Carvalho
arXiv:2608. 11690v1 Announce Type: new Abstract: Continual learning must absorb new tasks without erasing old ones, and replay---mixing a small buffer of past examples into current training---is among the most effective remedies for catastrophic forgetting.
By Tieliang Gong, Zhongbo Zhang, Wen Wen, Yong-Jin Liu
arXiv:2606. 08452v1 Announce Type: new Abstract: In many real-world settings, data streams are nonstationary and arrive sequentially, requiring learning systems to adapt continuously without retraining from scratch.
By Nazreen Shah, Govinda Arya, Bharath B. N., Ranjitha Prasad